Enabling cooperative adaptive cruise control on strings of vehicles with heterogeneous dynamics and powertrains

Abstract

Recent studies have shown that positive impact of Cooperative Adaptive Cruise Control (CACC) can only be guaranteed as market penetration rate increases. Removing the string homogeneity constraint is essential to encourage widespread adoption. In this work, a hierarchical architecture is proposed to enable CACC on vehicles with not only mixed dynamics but also different powertrain types. A low-level layer deals with the vehicle and powertrain dynamics to provide accurate and consistent reference speed tracking response. The high-level layer uses: (1) a Linear Parameter Varying feedback system to provide loop stability, robustness and enforce a variable time gap policy and (2) a feedforward system that processes Vehicle-to-Vehicle information to enhance string stability and response bandwidth, by dealing with the string heterogeneity. A gap management strategy is built on top of the CACC architecture to handle gap setting changes or cut-in/out situations, via a dynamics constrained time gap trajectory planning algorithm. The proposed work has been designed, developed and validated on three different real passenger vehicles on public highways and test tracks, showing the potential of the proposed algorithm to enable robust string stable CACC, despite the different dynamics and powertrains considered.

Keywords:

• Cooperative adaptive cruise control
• connected automated vehicles
• heterogeneous strings
• robust optimal control
• vehicle dynamics control

Acknowledgments

The authors would like to thank Dr. Hao Liu for its support in the experimental testing of the developed work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules

Additional information

Funding

This research was supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Re-search in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program under the direction of Mr. David Anderson, with support of the Project Manager Erin Boyd and Danielle Chou who are gratefully acknowledged.